Assessing Land-Cover Transformation and Seasonal High-Wind Exposure in the Context of Climate Change in Bauchi, Nigeria: A Multi-Source Geospatial Analysis | IJLTEMAS
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Assessing Land-Cover Transformation and Seasonal High-Wind Exposure in the Context of Climate Change in Bauchi, Nigeria: A Multi-Source Geospatial Analysis

Authors

Sani J. A.

Department of Environmental Management Technology, Faculty of Environmental Technology, Bauchi, Bauchi State, Nigeria (Nigeria)

Garba T.

Department of Urban and Regional Planning, Federal Polytechnic, Bauchi, Bauchi State, Nigeria. (Nigeria)

Ilelah K. G.

Department of Urban and Regional Planning, Federal Polytechnic, Bauchi, Bauchi State, Nigeria. (Nigeria)

Article Information

DOI: 10.51583/IJLTEMAS.2026.150900131

Subject Category: Climate Change

Volume/Issue: 15/9 | Page No: 2703-2715

Publication Timeline

Submitted: 2026-08-30

Accepted: 2026-09-04

Published: 2026-10-19

Abstract

Reliable local evidence on wind-related hazards remains limited in many rapidly changing West African landscapes. This study assessed seasonal high-wind exposure, land-cover transformation and hydroclimatic variability in Bauchi Local Government Area, Nigeria, using a multi-source geospatial design. Monthly NASA POWER maximum wind speed at 10 m was used to construct a high-wind proxy, defined as the number of months per year with WS10M_MAX ≥ 8.0 m s⁻¹, for 1985–2024. Landsat classifications for 1984, 1994, 2004, 2014 and 2024 quantified land-cover change, while gridded rainfall and temperature series for 2005–2025 described recent hydroclimatic variability. High-wind months were strongly seasonal and concentrated in December–May. The mean annual proxy frequency varied from 4.2 months in 1985–1994 to 5.0 months in 2005–2014 before declining to 4.5 months in 2015–2024; the linear trend was weak and not statistically significant (0.013 months year⁻¹, p = 0.481). In contrast, land-cover change was substantial: forest declined from 531.83 to 154.60 km² (−70.9%), cropland increased from 2641.38 to 2975.25 km² (+12.6%), and built-up land expanded from 33.85 to 72.27 km² (+113.5%). Rainfall was highly variable, including an unusually wet phase in 2020–2022, while the 2005–2025 temperature trend was not significant. The findings show that hazard exposure in Bauchi is shaped less by a simple long-term increase in regional wind speed than by recurrent seasonal high-wind conditions interacting with rapid land-surface transformation. Vegetation protection, climate-sensitive development control and locally validated wind monitoring are therefore central to risk reduction.

Keywords

wind hazard; land-use and land-cover change; NASA POWER; Landsat; climate risk; Bauchi

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References

1. Aliyu SJ (2026) Assessing the impacts of windstorm dynamics and vegetation change in Bauchi Local Government Area, Bauchi State, Nigeria. MTech thesis, Abubakar Tafawa Balewa University, Bauchi [Google Scholar] [Crossref]

2. Alkama R, Cescatti A (2016) Biophysical climate impacts of recent changes in global forest cover. Science 351:600–604. https://doi.org/10.1126/science.aac8083 [Google Scholar] [Crossref]

3. Bergametti G, Marticorena B, Rajot JL et al (2022) Rain, wind, and dust connections in the Sahel. J Geophys Res Atmos 127:e2021JD035802. https://doi.org/10.1029/2021JD035802 [Google Scholar] [Crossref]

4. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46. https://doi.org/10.1016/0034-4257(91)90048-B [Google Scholar] [Crossref]

5. Fitzpatrick RGJ, Parker DJ, Marsham JH et al (2020) What drives the intensification of mesoscale convective systems over the West African Sahel under climate change? J Clim 33:3151–3172. https://doi.org/10.1175/JCLI-D-19-0380.1 [Google Scholar] [Crossref]

6. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201. https://doi.org/10.1016/S0034-4257(01)00295-4 [Google Scholar] [Crossref]

7. Gelaro R, McCarty W, Suárez MJ et al (2017) The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J Clim 30:5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1 [Google Scholar] [Crossref]

8. IPCC (2022) Climate change 2022: impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781009325844 [Google Scholar] [Crossref]

9. Klein C, Taylor CM (2020) Dry soils can intensify mesoscale convective systems. Proc Natl Acad Sci USA 117:21132–21137. https://doi.org/10.1073/pnas.2007998117 [Google Scholar] [Crossref]

10. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259. https://doi.org/10.2307/1907187 [Google Scholar] [Crossref]

11. NASA POWER (2026) Data services and methodology. National Aeronautics and Space Administration Prediction of Worldwide Energy Resources. https://power.larc.nasa.gov/docs/services/. Accessed 29 July 2026 [Google Scholar] [Crossref]

12. Panthou G, Vischel T, Lebel T (2014) Recent trends in the regime of extreme rainfall in the Central Sahel. Int J Climatol 34:3998–4006. https://doi.org/10.1002/joc.3984 [Google Scholar] [Crossref]

13. Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20:503–510. https://doi.org/10.1016/j.tree.2005.05.011 [Google Scholar] [Crossref]

14. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934 [Google Scholar] [Crossref]

15. Taylor CM, Belušić D, Guichard F et al (2017) Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature 544:475–478. https://doi.org/10.1038/nature22069 [Google Scholar] [Crossref]

16. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150. https://doi.org/10.1016/0034-4257(79)90013-0 [Google Scholar] [Crossref]

17. US Geological Survey (2026) Landsat Collection 2 surface reflectance. https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance. Accessed 29 July 2026 [Google Scholar] [Crossref]

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